For the remainder of the experimental section, we compare with ResNet-50 [1]}, the canonical vision model, because many of the components that we ablate have been well studied for ResNet-50, allowing us to use best practices for the baseline model.
We tune our baseline ResNet-50 implementation to achieve a better accuracy, 77.6%, compared to commonly reported numbers in the literature. For example,  [2]} report 76.3%. We then create a new HaloNet architecture, HaloNet-50, that exactly matches the ResNet-50 architecture by replacing spatial convolutions with local self-attention.
HaloNet-50 and ResNet-50 have about \(18.0\)  million and \(25.5\)  million parameters respectively. We train both for 150 epochs on \(256\times 256\)  image size. We share other training details of the ablation set-up in the appendix
